Legacies of model initialization on predictions of future ecosystem dynamics in California's Sierra Nevada: insights from GEDI.
Gespeichert in:
| Titel: | Legacies of model initialization on predictions of future ecosystem dynamics in California's Sierra Nevada: insights from GEDI. |
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| Autoren: | Chang LL; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.; Department of Geography, Florida State University, Tallahassee, FL, 32306, USA., Liu S; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA., Antonarakis AS; Department of Geography, University of Sussex, Falmer, Brighton, BN1 9SJ, UK., Longo M; Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.; Divisão de Modelagem Numérica do Sistema Terrestre, Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, 12630-000, SP, Brazil., Tang H; Department of Geography, National University of Singapore, Singapore, 119077, Singapore., Armston JD; Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA., Dubayah R; Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA., Moorcroft P; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA. |
| Quelle: | The New phytologist [New Phytol] 2025 Dec; Vol. 248 (6), pp. 2809-2832. Date of Electronic Publication: 2025 Nov 11. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Wiley on behalf of New Phytologist Trust Country of Publication: England NLM ID: 9882884 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1469-8137 (Electronic) Linking ISSN: 0028646X NLM ISO Abbreviation: New Phytol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Wiley on behalf of New Phytologist Trust Original Publication: London, New York [etc.] Academic Press. |
| MeSH-Schlagworte: | Ecosystem* , Models, Biological*, California ; Climate Change ; Biomass |
| Abstract: | Accurate estimates of aboveground vegetation structure are essential for making reliable predictions of terrestrial ecosystem responses to climate change. However, traditional small-scale ground-based inventory methods cannot easily be scaled up to comprehensive, large-scale estimates of ecosystem structure. We assimilate remotely-sensed Light Detection and Ranging measurements of vegetation structure and corresponding imaging-spectrometry-derived estimates of canopy composition into the ecosystem demography (ED2.2) terrestrial biosphere model across an elevational transect in California's Sierra Nevada. We then used the model to assess: how incorporating observed ecosystem structure and composition influences predictions of ecosystem change over the coming century as compared to simulations initialized with long-term potential vegetation; and how ecosystems are predicted to respond differently to future climate change. Our analyses show multi-decadal impacts of initialization on predictions of ecosystem composition and structure, emphasizing long-term legacies of climate and disturbance history in predictions of ecosystem responses to climate change that are not captured when models are initialized with outputs from long-term historical simulations. The remote sensing-initialized simulations predict increases in aboveground biomass and leaf area index, and pronounced elevation-dependent changes in canopy composition. The differences among initialization methods, climate scenarios, and elevational gradients have important implications for improving ecosystem modeling and informing land management strategies. (© 2025 The Author(s). New Phytologist © 2025 New Phytologist Foundation.) |
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| Grant Information: | #80NSSC21K0197 United States NASA NASA; #80NSSC21K0197 United States NASA NASA |
| Contributed Indexing: | Keywords: California's Sierra Nevada; Ecosystem Demography Model (ED2.2); GEDI; climate change; ecosystem composition; ecosystem structure; future predictions; model initialization |
| Entry Date(s): | Date Created: 20251112 Date Completed: 20251120 Latest Revision: 20251120 |
| Update Code: | 20251121 |
| DOI: | 10.1111/nph.70657 |
| PMID: | 41220168 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Accurate estimates of aboveground vegetation structure are essential for making reliable predictions of terrestrial ecosystem responses to climate change. However, traditional small-scale ground-based inventory methods cannot easily be scaled up to comprehensive, large-scale estimates of ecosystem structure. We assimilate remotely-sensed Light Detection and Ranging measurements of vegetation structure and corresponding imaging-spectrometry-derived estimates of canopy composition into the ecosystem demography (ED2.2) terrestrial biosphere model across an elevational transect in California's Sierra Nevada. We then used the model to assess: how incorporating observed ecosystem structure and composition influences predictions of ecosystem change over the coming century as compared to simulations initialized with long-term potential vegetation; and how ecosystems are predicted to respond differently to future climate change. Our analyses show multi-decadal impacts of initialization on predictions of ecosystem composition and structure, emphasizing long-term legacies of climate and disturbance history in predictions of ecosystem responses to climate change that are not captured when models are initialized with outputs from long-term historical simulations. The remote sensing-initialized simulations predict increases in aboveground biomass and leaf area index, and pronounced elevation-dependent changes in canopy composition. The differences among initialization methods, climate scenarios, and elevational gradients have important implications for improving ecosystem modeling and informing land management strategies.<br /> (© 2025 The Author(s). New Phytologist © 2025 New Phytologist Foundation.) |
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| ISSN: | 1469-8137 |
| DOI: | 10.1111/nph.70657 |